Ioana Teleanu is an AI product design expert and consultant who shaped the experience of Miro AI as their Lead Product Designer. She is currently the creator and instructor of AI for Designers, one of the most successful courses launched on Interaction Design Foundation. 

Moments before sitting down for this episode of Product Momentum – recorded live at ITX’s 2025 Product + Design Conference – Ioana delivered a keynote in which she invited attendees to reflect on the evolving relationship between AI and UX design. Our discussion explores the realities and myths surrounding AI’s impact on jobs, the challenges teams face in adopting AI tools, ethical considerations in deploying AI, and practical advice for integrating AI into design workflows.

Here’s what you’ll learn:

AI and the Future of Design Roles

Ioana puts to rest concerns about AI replacing product managers and designers, rejecting as “clickbait” the many recent headlines that provoke anxiety and stress in the industry.

“AI is transforming our roles, but they’re not going away,” she says. Referring to a study by the World Economic Forum, Ioana believes that “AI will end up creating more jobs than it replaces. There’s going to be more jobs thanks to AI, and one of the professions that was marked as growing is UX design.”

Addressing Challenges in AI Literacy and Adoption

Design teams face loads of challenges these days that postpone adoption and integration of AI into their workflows – many of which are rooted in decisionmaking at the organizational level.

As leaders, Ioana advises, we need to give our teams permission to play around with these tools. “Our teams are in a very odd place right now; there’s a great deal of expectation, but not a lot of support.” Company leadership needs to provide the resources – e.g., time, context, and financial support – to create the structured environments their teams need for AI learning and experimentation. “The cost of breaking things by experimenting with AI is far lower than the cost of not experimenting at all,” she concludes.

Ethics and AI’s Missing Moral Compass

Ioana reflects on society’s gradual relaxation of privacy boundaries, noting that people frequently share intimate information with AI systems that they keep even from their loved ones.

“AI doesn’t understand consequences,” she says. “It doesn’t have a moral compass. As designers, that means the responsibility is even higher on our side to protect our users and to keep their data safe.”

Final Thoughts

• Contribute to the public disambiguity. AI technology is infant technology. We’re all trying to find the answers. If you do something interesting, talk about it.
• Context (in prompt writing) Is Key. Train AI tools on specific design systems or product aesthetics to improve consistency and reliability.
• Synthetic users. They may never replace real users, but they might help UX researchers get a quick sense of something before we test with real users.
• The (Blurring) Line between Product + Design. Catch Ioana’s real-life example of a product manager (trained in Design) who initiated company-wide Figma training for product managers.

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Sean Murray [00:00:09] Hey everyone, welcome to Product Momentum. I’m Sean Murray, Director of Product Management here at ITX.

Dan Sharp [00:00:15] And I’m Dan Sharp, a Product Manager at ITx. It’s our privilege to continue the product momentum journey, a platform that celebrates the thinkers and doers who are shaping the future of software development.

Sean Murray [00:00:25] Our goal is to introduce you to industry leaders, innovators, and practitioners, and bring you their insights, advice, and innovations. We talk with experts from product strategy, UX research and design, security, engineering, leadership, and more. We’ll discuss how their approaches to problems help empower teams and build trust. And we’ll explore trends, tools, and techniques that your team can use to deliver the software solutions your users need today. So tune in, stay curious, and join the conversation.

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Dan Sharp [00:01:14] Christina, we just had an awesome conversation here at the ITX Product + Design Conference with Ioana Teleanu. We did. I would love to hear what your takeaways were.

Christina Halladay [00:01:23] Awesome. Yeah, Ioana has an awesome perspective. I think she’s seen a lot in a lot of different capacities and so she brings a really level-headed approach to things in a time when there’s so much uncertainty and anxiety and she has a lot to share around that.

Dan Sharp [00:01:40] Yeah, I love that. And I love that she was bringing just how AI is going to be a benefit to humanity.

Christina Halladay [00:01:45] Design is not dead.

Dan Sharp [00:01:46] It’s not dead. Super awesome conversation. Let’s get to it. Special co-host, Christina Halladay. Christina is a lover of all things design, got her start as an interaction designer. And now here we are about 10 years later, she is a delivery director at ITX and crushing it. We’re doing some product-led, design-led teams here. And here we live at the Rochester Riverside Convention Center. Just came off of an awesome keynote. We have our special guest, Ioana Teleanu. Super happy to be here. So we heard about it in the opening, but Ioana, you are a product lead for Miro, application that I have come to know and love working in AI there. You specialize in AI design, AI products. Before that, you were at UiPath, received the Time Magazine Best Invention of 2023 award, which is pretty awesome. You’re also an instructor. Um, so we are super excited to have you here talking with us about design and AI. So we’re going to jump right into it. I was on LinkedIn and it seems like product managers and designers. Our jobs are gone. AI is going to take them. Really curious to hear what your thoughts are there.

Ioana Teleanu [00:03:14] Yeah, hi. First of all, hi, thank you for inviting me. I’m very excited to be in Rochester. I’ve had a very nice experience of the city so far. I think it’s fascinating and it’s just full of strong narratives about the world and I’m really, really grateful to experience all of this and just want to make a quick amendment to what you said. I am not with Miro anymore. I was there as the first AI product designer for one year and something. And then I decided that I want to expand my problem space, diversify the problem spaces I work in. And I opened my consultancy studio, which yes, it exposes me to a lot of new types of problems that companies have, but I’m learning that many times it comes down to the same couple of patterns or yeah, just common, I wouldn’t say mistakes, but maybe pitfalls that you see when you try to approach AI.

Now, getting back to your question, I didn’t forget what you asked me. No, I don’t think the design role is dead by any means. I see those sensationalist headlines and very controversial takes. And I’m also a content creator since 2018. I have many followers. And I’ve learned by being in the space that a lot of people just optimize for virality, they optimize for controversy, they just write these headlines for engagement and to kind of tap into that emotional anxiety and that makes you read through or react or ask questions or just, yeah, engage in a way with their posts. So that’s a lot of clickbait.

But what’s true is that our roles are indeed transforming. Even though I spend a lot time immersed in the space, I can’t say. Where we will end up, nobody can know essentially what is the, I mean, we see some parts of it. I understand the nature of how some parts of our roles will change, but I can’t tell you this is what we’ll be doing in five years from now because technology is still surprising in a way, right? So it’s a process of figuring out how to disambiguate all of this. But our roles are transforming, they’re not going away.

There’s even a study by World Economic Forum that I read recently, where they talk about the future of jobs as their annual report or something. And their thesis, based on all the research that they did, is that AI will end up creating more jobs than replacing. So it’s like, I don’t know, 170 million created and 100 something. Displaced, whatever, but the, there’s going to be more jobs thanks to AI. If we look back at industrial revolutions, that might be the case. So history kind of confirms that we just transform or we invent, create new jobs, new things to work on. But so AI might create more jobs. And one of the professions that was marked as growing is UX design. And that was really interesting because it got me thinking like Why? Because if the problem spaces are contracting, how can it be a higher need for design? And my assumption answering this is that we’re creating a lot of design depth as we’re moving through this technological revolution. We’re making decisions mostly through engineering, just deploying things and experimenting or leadership that’s rushing to not miss out on the AI race. And so there is a lot decision making that’s chaotic. And I think it’s the nature of this moment in history. And that’s okay, maybe, but it also creates, it will create a lot of poorly usability experiences. And so designers will, and also engineers will have to fix the code that AI is producing, but designers will have fix the design mess that these systems will probably put out there in the world. And so, yeah, I’m not really worried.

Christina Halladay [00:07:30] We’ve been here before. It sounds familiar. It’s like a lot’s changed, but a lot remains the same. And I love your very down-to-earth perspective on it. I think I, I don’t know about all of you, but I am in need of some reality, I think, with all the anxiety and the hype. So it’s great to hear that. And you’re uniquely positioned, because between your consulting and your instruction and your past experience, you’ve seen quite a bit on the ground. You know, where we talked a little bit about, you know where things might go in the future, but like today, where are we today? What are you seeing today? Like what’s table stakes for AI literacy and adoption in teams today?

Ioana Teleanu [00:08:12] Yeah, it’s a great question. And I’m happy you framed it like that because it helps me frame my answer very clearly. So part of my work as a consultant now is to come in and design teams and kind of create structures, frameworks, and a platform for them to experiment with technologies or at least give their designers and their team leaders or head of design. Give them permission to just play around with these tools because there’s a lot of just reluctance or this feeling of stuck.

I want to AI, but how do I AI? Where do I start? If it’s not facilitated by my company, do I just, like I was saying in my keynote, we don’t wake up one day in the morning and say, what do I do with AI today? Yeah, nobody starts out like that. So it needs to be facilitated. Or again, as I was mentioning in my, keynote to an extreme, the intercom example of just forcing designers to vibe code. And so again, forcing, just putting them in a room with like clearing up their calendars, which is also an important part of enabling teams to improve their AI literacy is to clean up their calendar. Like one of the things, this is another layer of things we’re expected to do. Like adjust, experiment, learn, find time to try out lovable, find time to try out. Figma make or whatever, but when? Cause we also have the same level of pressure, even higher. The pressure is higher than ever because a lot of designers have been laid off. Yeah. The teams are contracting currently. There’s also the pace of building and launching features that’s changing. It’s worse than ever. Like we really need to move fast. So there’s pressure from all sides. And then you also have to keep up to date with those technologies. And this is I feel, a leadership policy, a problem of leadership failing, there was a study published by The Designer Fund and they surfaced the fact that 7% of companies have budgets to train their design teams in AI. So nobody’s prioritizing it, but it’s expected. So it’s really strange, it’s a strange time. And I think that we should come together as an industry and just say, look, we need you to support us. We need budgets for it. We need, I need the annual training budget to be used on. I don’t know what prototyping workshop or whatever. So that’s really important because otherwise you’re just gonna have these sparse all over the place efforts, very individual that don’t amount into something that really infuses, pushes the entire team forward. You have to create frameworks for exchanging all these insights between the teams. So I think teams are in a very odd place right now where there’s a lot of expectation from them but not a lot support.

However, by working, and I’m working with teams a lot on that, like from Atlassian to the government of UK to, I was just talking, I’ve launched a series on my podcast, which is exactly about that, how to AI. And I’m inviting leaders from different big tech names, but also I will invite smaller. Companies, because I think they also deserve a voice and we need to learn what’s more. I’m like, why do we only talk about Apple? And so I will have the different type of leadership joining to talk about their efforts around AI. And in the first episode, the second episode was with Henry, the VP of design at Perplexity, which is also the founding designer at Perplexity. And he had a very different story about using AI than the first guest, which was a leader of design at Intercom. That was supporting this kind of creating the cursor environment and then experimenting with it.

And so, but there were different worlds in a way about like one company was AI first, like Perplexity is an AI product that lives thanks to AI. Intercom, they’re doing the, I don’t know, thin their agent, the power by AI and whatnot, but it’s not the AI first company. So the, the ways in which they were infusing AI in their teams were vastly different. So, and with Perplexity. That’s what you do, you AI, you have to use it, you have to get it, so it’s implicit. In other teams you have to create these structures in place. And Intercom is also a very good example. Most companies I talk to, they’re in that level of paralysis. Like we want to do something, but we don’t know what to do. And we don’t know where to start and we don’t have the time. And there’s a mandate that we do AI, but I have to deliver, I don’t know what feature by next week, right? So it’s a weird place and you have a gradient of stages or situations that these companies are in. Few are really strongly experimenting and creating the platforms for it, but we’ll see. Yeah. I think the market will catch up eventually.

Christina Halladay [00:13:15] That’s the hope, I think. I’m definitely sensing that. I’ve had a lot of conversations the last couple days with people, and of course I’m asking, well, how are you using AI? And there is a lot that pressure where it’s on. I feel behind, or we were in a workshop yesterday playing around with Lovable and other tools like that, and half the room didn’t have access because they couldn’t access the tool, and it’s like, on one hand, it’s use the tools. Be more efficient, work faster, be relevant, and then it’s like, oh, but you can’t, you can’t. I know, I see the thing. It’s a tough balance.

Ioana Teleanu [00:13:50] I see same things in the wild, yeah.

Christina Halladay [00:13:54] What would you say to individual contributors or maybe even leaders of a team that want to advocate for themselves to have more access to training, to tools, whatever it might be, because they see where things are going, they’re hearing the demand, and they want to do something about it.

Ioana Teleanu [00:14:11] I think it’s, I can’t think of a unique thing that works because it’s very dependent on the type of company and like the type fear that they’re running on. So many companies are in this very competitive mindset that they just want to have AI because their investor is demanded or whatnot. Other companies are just like, well, you know, we’re doing great without AI. We don’t need it. But you still the angle of argumentation should be adapted to what the leadership values or is afraid of. As a general rule, I feel that the cost of breaking things by experimenting is lower than the cost, of not experimenting. So many of them are reluctant because they don’t want to, yeah, just waste resources with like investments in AI and in training and setting up the systems and then it amounts nothing. I think they have to own the fact that it might amount to nothing, but it never does because there’s the learning aspect to it, which is valuable, whatever happens to that project. So in a way, not trying, not spending money on it is not saving money. Doesn’t mean you’re saving money or you’re losing more money. And yeah, I think also sometimes the leadership is just very stressed out as well. They have pressure from all sides and they just need someone to tell them, hey… We have to do something that you’re probably, it’s not what keeps you up at night, but it’s important. And many of them, I think, should be supportive of it.

And then I think most companies, especially larger companies, but I think all sizes, have learning budgets. If you just have to move that, like, re-route those budgets to AI stuff or other budgets, like just move around money in this new bucket of experimentation and learning and make it like, I don’t know. It’s not that hard to create safe environments to use GPT in your company.

And I also gave a workshop in Berlin with a bunch of designers from all sorts of companies and sizes and seniority and everything. And they had the same problem. They couldn’t access GPT. And yeah, sometimes they were coming up to me during the workshop. Can you put in this prompt in your computer? And I’m like. Sure, I can do that. But do you have no interface that you can prompt? Like that’s the most, right? Do it on your phone. I don’t know. But it’s like, I understand the concern. And even when I was working at Miro, we had fears from our biggest clients that data that they put to Miro about their products, their vision, their strategies, kind of inform those systems. And then everybody can ask GPT, Hey, what is Apple working on? That’s right But there are ways, like I’m not a technical risk person, but I know working with these people that you can put guardrails and safety measures and create an environment that’s closed and the data is protected. And I think there’s an opt out of training on your data that’s like even embedded using their API. So yeah, I don’t know. I think, there are a ways. You just have to, where there’s a will, there’s way. So. They just have to want, and sometimes I think they want, but they don’t think about it.

Dan Sharp [00:17:33] To have a great follow-up question out of the audience. Right, in your keynote, you’re a big proponent of ethical AI, making sure that what we’re doing is benefiting humanity. Absolute question, right? Huge challenge for AI users is erosion of privacy. A, how do you feel about this? But also, right, what can designers, users, also product managers do to make sure that we’re maintaining privacy boundaries?

Christina Halladay [00:17:58] That’s a big one.

Dan Sharp [00:17:59] You can go for another hour, it’s okay.

Ioana Teleanu [00:18:01] Yeah, it’s a great question. It’s a very good question. It’s very philosophical as well. It is also very practical, but very philosophical.

Dan Sharp [00:18:10] This is the after lunch session, so it’s philosophy time.

Ioana Teleanu [00:18:13] Yeah, I’m happy. I love the question. So I don’t even know where to start on it, but I’m just gonna go ahead and yeah. So the thing with privacy is that we have increasingly lowered our protection mechanism. Like we are so accustomed to location services, tracking everywhere we are, iPhone knowing everything, we’re taking photos of everything. Where, like, we have flexib… We’ve made flexible these boundaries of privacy and where we take this reality for granted. So the way we interact with AI systems is just pushing those boundaries a little bit further, right? So now we’re asking very private questions, maybe very private health questions, very problems from our relationships.

I saw a recent study of how people use AI systems and In 2025, the highest use case is therapy and companionship. Like people really have very intimate conversations about their emotional problems with the system. So we don’t feel worried. We’re not thinking about it so much, right? So, and that’s really weird in a way, because at some point we were very. It was a big conversation, like how do we protect privacy, that cookies on the internet, we decline them. I don’t want you to know that I was here on this website. But now we tell GPT, look, I had a fight with my partner this morning and he told me this, am I right or is he right or who’s right? And of course, GPT will always tell you, you are right, but with a reflective angle, like his point of view is not completely wrong, but you’re right. So, and that’s also a problem, right? Is it making us more and more narcissistic? Like if we have this pocket companion that’s approving of everything we say and think and do, like it’s not as obvious as it used to be. It used to even more of a cheerleader to whatever we gave it. Now I think they’re working on making it more nuanced. I think the pulled back a model update that made it very polite and. And so they’re, they’re really interested in finding that sweet spot between being accepting of whatever you tell it, but also kind of not, um, encourage murder or anything, right? Cause it’s, it doesn’t understand consequences. It doesn’t have a moral compass, right. We teach it, But it could like, if it doesn’t get the risk of whatever.

And I know there was recently a case where the police arrested a guy that was talking about his intentions with GPT and so does the system mark that as look this sounds really really sketchy and the team alerted the police and they arrested him and yeah so I’m very far away from what you ask me about privacy but my point is that some people tell their murder intentions to GPT so they’re most the dark hidden secret it’s out there and they don’t mind and so What does it mean? It means that the responsibility is even higher on our side to protect them, to keep that data safe, to even maybe, I don’t know, you don’t wanna judge when someone, like as an entity, and now I’m an AI system, and someone tells me something, and I don’t wanna tell them. That’s inappropriate, or right? You don’t want value judgements, but maybe those systems should reinforce some sort of, right, like maybe that’s not a topic, maybe discuss it with your therapist, or, right? So maybe these systems can have… A moral compass, like the moral, ethical structure in place to stop us when we’re, right? Because stories of people falling in love with GPD or like it’s, then it’s just the beginning. Like we’ve all seen Her, the movie. Yep. It’s, we’re gonna have that because they’re so nice to us. It’s hard not to like them, right. Sometimes GPT makes me smile. Like it cracks a joke, or it has like a, and this is just the beginning, it’s infant technology. It’s only gonna get better, it’s only going to get to know us more.

Christina Halladay [00:22:36] And so, where is this all heading? You mentioned in your keynote, these guiding principles, right? And you’ve kind of put these together. And I loved what you said too, when you kind of framed these up, you were like, listen, this is from my experience. I’m probably missing something. I don’t know. And I think that’s kind of the vibe. We’re all figuring it out. But I do love the idea of principles. And that’s something that as designers we’re familiar with. That’s a concept that we put in place, right. And so I love that even a myth. With all this uncertainty, you are pulling on things that we can rely on, that we know, that we look to to kind of guide us through this, because there is no answer, you know? And I just wonder what other frameworks are going to emerge, too. When you were talking about principles, I immediately was like, oh, my God, do we start accounting for this in design systems and guides and things like that? And I had this train of thinking, and there’s going to be so much of that that we’re going to start to see. You know? Yeah.

Ioana Teleanu [00:23:32] Well, I don’t know if it’s a question. I read it as a question, I turned it into my own question. I remember when I was working at Miro, I had, I think, I shared a post about a design decision or a new feature we launched, which was some sort of a way in which you could jump from one workflow stage to the next. Like you did your resource synthesis and now you have to share it out with your leadership or team. And so with one click, you would go to the stage. I was talking to Jacob Nielsen. I talked to Jacob Nelson. Wow. Wow. And he told me you have to make this public, you have to make all that, all the design process behind it. You have to document it. It felt like he was scolding me because I’m like keeping the kind of new problems we have to myself. And I was like, is this really worth writing about? Yeah, I think it’s a new interaction. It’s called Accelerators and he found a name for it. I was like, whoa, I designed something that’s like, yeah. And so the point I’m trying to make with that is that we should all contribute to this public disambiguity. If you do something interesting, even if it doesn’t feel to you like you did something, talk about it. And then collectively, we document and gather these decisions, the rationale behind them, the patterns, the interaction models and so on. That’s all I have.

Dan Sharp [00:24:58] Got another one from the audience here. Just kind of bringing it back to the day-to-day practical advice. What can you give to the designers out there using AI tools? We saw yesterday in the workshop, six tables put in the same prompt, got six different UI outputs. How do we kind of deal with that?

Ioana Teleanu [00:25:18] Yeah. Well, I think it’s a problem of context, but like a specific problem of context. So I think we were talking earlier about how prompting or I was talking with Luke Stiles, prompting is a small part of it. Context is the bigger problem when you’re working with the AI system. So you have to prompt it well, but if the context is not good, whatever they do, it’s not going to be.

It’s like, there’s a saying that we keep using. Garbage in, garbage out. So you really have to give a proper context. So what that means for UI is in a way, teach it on your product, on your design system. And I know that many years ago, like when I started giving talks about AI in 2022, I think, at Figma, at config in San Francisco, Uber had a keynote and they show that they’re working on an internal tool that would let designers Text prompt by texting their design system, and then create designs in the language of their design systems. So everybody could kind of come in and say, add me a button, do whatever, because it was educated on their design. And at that moment, it felt like, Whoa, that’s so like these Uber guys, they’re so, they they’re in the future, man.

And, and, and now it’s like, this is becoming a reality, like for large design teams and with complex languages. We’re probably, I think now we have to train these internal tools on the design system. But in the future, it can just learn from your product. It can be the design, system. It can keep the product in check. Like the, this AI agent, the design-system agent can go on your website and say, there are some discrepancies between the roundedness of your buttons and you should fix this, this and that. And so they’re going to be the gatekeepers of consistency. And the design system. So this is why the UI varies, because it doesn’t know how to look like. I mean, it knows what to represent, but it doesn’t know how to represent it. So if you give it the how, and sometimes I think they’re smart enough that if you’re giving them a screenshot from your product that has more elements in an empty page, like a 404 or whatever, give them a screen shot from your main page, dashboard, whatever, then it can kind of figure out, okay, so this is the esthetic visual we’re going for, but yeah, it’s still it’s hard.

One of the things we struggled with when we were assessing AI success at Clipboard AI was the repeatability aspect. So can you, if people do copy this invoice to this system a hundred times, how much of a variability will be in there? Because it shouldn’t be, because they want to do something that they can trust. Acts the same way again and again. And so we were really testing for repeatability. We were testing, this was a metric in our system. Now, I think they call it evals for product managers is how you evaluate the system, the AI system. But for designers, we were just like testing. I had this confluence page where I documented what the AI did every time. And it was like, I was testing maybe 30 times, but it’s like, you can get a hundred different things. So. They’re also, the learning is very difficult, right? But I think you can have an AI that tests a hundred times. Like you can use AI to test. And there are tools, like there’s a recent tool I talked with and they have a Figma plugin that runs through your prototype and acts like a real user in the sense that it talks out loud. So it says, oh, this page, I’m supposed to, I think I’m suppose to do that, but this is not so it’s. Or simulates the user behavior and it tests for two things. Task success, like can the user do whatever you instruct because you give them the scenario. Like in a real testing session, you say, you have to check out, buy a dress. And so it tests if you can buy a dress and it also looks at accessibility problems. And now they’ve added this feature where you can test 10 times and probably you will be able to say test 200 times. Yeah. So you will be able to learn again. There’s a huge ethical conversation to that. Like this is just the first layer of mapping small problems, obvious problems, and then once important life-impacting changes, you have to test with people. Yeah. But it’s interesting Do the learning aspect, right? So we’ll see how that evolves.

Christina Halladay [00:30:03] Going back to the user. Get in front of your customer.

Ioana Teleanu [00:30:07] Yeah, this will not change, this is not change. There are, there’s a product called synthetic users getting a lot of hate. I’m friends with the owner, so the founder, so I’m biased but he gets a lot hate. The first time I talked to him, he gave me a list of words he got from people. Like he was called in every possible offensive way by the, in like, especially by researchers that are most triggered by this concept of a synthetic user. However, it’s the framing. It’s just a framing problem. He doesn’t say that synthetic users can replace real users. He says that you can get a quick sense of something before you bring things in front of the users. I don’t know. It is a very difficult conversation. I do not know.

Christina Halladay [00:30:57] No, you’re right, and I think that applies to everything, right? I think also in a similar way, designers get a little itchy when we hear about product people making design prototypes, you know what I mean? But I had a lot of conversations where I was like, I could get on board with that. Someone was telling me, using it to help with estimation and scoping. This isn’t good if we’re not doing anything with this, but tell me what you think about this so you have a better idea, right, because that’s always a tough conversation to have.

Ioana Teleanu [00:31:26] And what I want to add to that was that like my partner works as a product management and he, at some point, because he comes from a design background, he created this design prototype in Figma, no AI involved. So this is a very conventional tooling story. And so he creates this Figma prototype and his director of product, BPO, whatnots, but my God, this is great. All product managers should do this. We should have a Figma training. And so, he starts this, he rallies this initiative. Where he trains product managers to use Figma to create their own prototypes. So it was a systemic progress thing in this company to just not rely on designers to create. So, and there was no AI involved. So the point I’m trying to make is that this blurring of lines and this tension has been around for a while. Like, I still see this very old problem that hasn’t been resolved. Of designers being called at the end of the process. Can you create a design? Yeah, this is what needs to happen. And we haven’t figured that out. So AI is just, I don’t know, another way to experience the same problem, but it’s not a new problem.

Dan Sharp [00:32:37] Yeah, I love that. I feel like it’s another tool for us to have better conversations. Yeah. And we can’t replay like AI is not getting rid of that. Yeah. So it has been fabulous having here. I’ve learned a ton. Thank you for coming on the pod, Christina.

Dan Sharp [00:32:55] That was quick. Yeah, that went good. All right, awesome.

Christina Halladay [00:33:03] Thanks!

Dan Sharp [00:33:04] Wow.

Speaker 6 [00:33:11] Well, that’s it for today. In line with our goals of transparency and listening, we really wanna hear from you. Sean and I are committed to reading every piece of feedback that we get. So please leave a comment or a rating wherever you’re listening to this podcast. Not only does it help us continue to improve, but it also helps the show climb up the rankings so that we can help other listeners move, touch, and inspire the world, just like you’re doing. Thanks, everyone. We’ll see you next episode.